어떤 PDF든 읽지 않고 이해하세요.
요약, 핵심 포인트, 개체 추출이 모두 브라우저 안에서 — 서버 없는 온디바이스 LLM.
더 똑똑한 읽기, 업로드 제로.
로컬 LLM이 문서를 읽고 몇 초 만에 구조화된 인사이트를 제공합니다.
100% 프라이빗
문서는 기기를 떠나지 않습니다. 추론은 WebGPU로 로컬 실행됩니다.
두 가지 오픈 모델
SmolLM2 1.7B 또는 Qwen2.5 1.5B 중 선택 — 작고 빠르고 뛰어납니다.
긴 문서 지원
긴 PDF는 부분별로 요약한 뒤 최종 브리프로 통합합니다.
구조화된 출력
마크다운 형식의 요약, 포인트, 개체 목록을 어디든 복사하세요.
Three steps. Zero servers.
- 01
Upload a PDF
Text is extracted page by page in your browser via pdf.js. Nothing is uploaded.
- 02
Pick a task & model
Summary, key points, entity extraction, or your own question — answered by SmolLM2 or Qwen2.5 running locally.
- 03
Read the brief
Answers stream in as they are generated, ready to copy as Markdown.
About the PDF summarizer
A free tool that summarizes PDFs and answers questions about them with a language model that runs on your device — no API keys, no uploads, no per-page fees. It is aimed at students, researchers, and professionals who need the gist of long papers, reports, and documentation quickly.
How it works
pdf.js extracts the text layer locally, then the document is chunked and mapped-reduce style summarized by SmolLM2, a small language model running in the browser via Transformers.js. In QA mode, the most relevant passages are retrieved and answered from the document itself. Progress streams live, and results are copy-ready or exportable as Markdown.
Limits & requirements
On-device models are small by design: they summarize well and answer factual questions grounded in the text, but they are not a frontier chatbot for open-ended reasoning. Only text-based PDFs are supported (scanned PDFs need OCR). Chrome or Edge with WebGPU is strongly recommended; model weights download once and are cached.
Privacy
Papers, contracts, and notes never leave your machine. Summarization runs locally after the one-time model download, so documents that would be reckless to paste into a cloud LLM can be summarized safely.